The impact of the logistics performance index on global trade volume between the republic of Korea and major GVC reconfiguration participants in ASEAN
Bibliographic record
Abstract
Logistics’ significance in international trade is being noted more and more frequently. This study was conducted to analyze the influence of logistics performance on trade volume between the Republic of Korea (ROK) and member states of the Association of Southeast Asian Nations (ASEAN) in order to identify the areas of the Indonesian logistics industry that require improvement to increase trade volume between Indonesia and the ROK. This study focuses on Indonesia, Vietnam, Malaysia, Thailand, and the Philippines, which are actively responding to the reconfiguration of the global value chain (GVC). The report also includes Cambodia, Laos, and Myanmar, which can be viewed as potential GVC competitors of Indonesia due to their considerable manufacturing growth potential. Based on the gravity model, which explains trade volume between regions, this study looked into the effect of the logistics performance index (LPI) of these ASEAN nations on trade with the ROK by analyzing panel data. This study utilized previously published (secondary) data to derive new outcomes. Most of the statistical data were extracted from the World Bank database, IHS Markit, and Euromonitor. The results show that an improvement of LPI can lead to growth in the trade volume between ROK and ASEAN Nations including Indonesia. The study’s insights suggest which logistical areas Indonesia should focus on developing in order to boost trade with ROK and obtain a competitive edge in the GVC reconfiguration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".